Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models: (a) Linear Regression Analysis (b) Log-linear Multiplicative Model

1. Market Mix Modeling:
Search For The Perfect Mix
5th Annual Big Data Conference (2017), Santa Clara
Amit Satsangi
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2. Amit Satsangi
https://www.linkedin.com/in/amitsatsangi/
• Data Scientist (Consultant)
• 10 years: Analytics, IT and
Management experience
MS Physics  MS Comp Sci  MBA

3. 3
Online channels and Revenue Allocation

4. Problem Definition
4
DisplayAds/Paid Search (SEM)
Email
Television Ads
Print Ads
PR Events

5. 5
Attribution: Assigning $ Value to online events

6. Assisting Channel
(SEO)
Originating Channel
(SEM)
Assisting
Channel
(Email)
Activity Marketing
Spend
Day1:
User searches for a beauty
product, and clicks on Google Ad
SEM
($16)
Day 5:
Organic Search by user, lands on
the company website, provides
email, abandons cart
SEO
($5)
Day 6:
User receives email and clicks on
it, but still does not buy
Email
($0.05)
Day 7:
User sees ad on Facebook, clicks
on the ad, and makes a purchase
Revenue Generated $100
Facebook
Ad
($2.0)
Converting
Channel
(Facebook)
The Channel Attribution Problem

7. Multi-touch Attribution Modeling
7
Facebook
All
???
First-click
Last-click
Linear .
Time Decay
SEM

8. 8
Attribution Models  Media Mix Optimization 

9. Issues With Multi-touch Attribution
Modeling
9
Soccer
How should we distribute the prize money of $10 Million amongst these players?
• Give 100% to the goalkeeper(First Click Attribution)?
• Give 100% to the player who hit the ball in the goal (Last click Attribution)?
• Something else???
Does This Sound Totally Absurd?
So, should Attribution Modeling

10. Let’s Talk About Sex
10
01
Detector

11. 11
Marketing, Promotion & Advertising 101

12. Patterns of Advertising Response
12
02
01 Current Effect
Carryover Effect
03 Shape Effect.
04 CompetitiveEffects
37%
Dynamic Effects05
06
07
Content Effects
Media Effects

13. Temporal Effects of Advertising
13
Modeling Marketing Mix
Gerard J Tellis

14. 14
Modeling The Market Mix Modeling Problem

15. Market Mix Modeling: A Few Techniques
15
Modeling Marketing Mix (Gerard J Tellis)
1. Basic Linear Model
A, P, R, Q: Email, Price, Display Ads, SEO, TV
Paid Search, Print
2. Multiplicative Model
3. Logit Model
Sales = 169 + 2.5*Email + 45*Price + 3.5*TV + …

16. 16
Market Mix Modeling : Experimental Results

17. Input Data
17

18. Results From The Linear Regression Model
18

19. Results From The Log-linear Multiplicative Model (I)
19

20. Results From The Log-linear Multiplicative Model (II)
licative Model
20

21. 21
Learning is the eye of the mind…

22. • Channel Attribution fails to provide even a minimally acceptable
solution to the problem of Media Mix Optimization (marketing
budget allocation)
• At best Channel Attribution can be used to determine the most
popular “channel paths” that the customers take (using Markov
Chains)
• Using MMM one can go to various levels of model complexity
depending on the effects that are desired: current vs. carryover
vs. shape etc. (see Tellis)
• With MMM the costs of building and keeping the model up-to-
date go up with complexity (accuracy)
• Any MMM model (even the most basic one) takes time and
effort to build – check for Heteroscedasticity, Collinearity,
Normality. Auto-correlation etc.
Learnings
22

23. It is not the answer that enlightens, but the question.
…Eugene Ionesco